Recent human vision research suggests modelling preattentive texture segmentation by taking a set of feature samples from a
local region on each side of a hypothesized edge, and then performing
standard statistical tests to determine if the two samples differ significantly
in their mean or variance. If the difference is significant at a specified level
of confidence, a human observer will tend to pre-attentively see a texture
edge at that location. I present an algorithm based upon these results, with a
well specified decision stage and intuitive, easily fit parameters. Previous
models of pre-attentive texture segmentation have poorly specified decision
stages, more unknown free parameters, and in some cases incorrectly model
human performance. The algorithm uses heuristics for guessing the
orientation of a texture edge at a given location, thus improving
computational efficiency by performing the statistical tests at only one
orientation for each spatial location.